Inductive Game Theory and the Dynamics of Animal Conflict
Publication Date
May 13, 2010
Journal
PLOS Computational Biology
Authors
Simon De Deo, David C. Krakauer & Jessica C. Flack
Volume
6
Issue
5
Pages
e1000782
DOI
https://dx.plos.org/10.1371/journal.pcbi.1000782
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000782
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/20485557
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869306
Europe PMC
http://europepmc.org/abstract/MED/20485557
Web of Science
000278759700014
Scopus
77955943664
Mendeley
http://www.mendeley.com/research/inductive-game-theory-dynamics-animal-conflict
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Mendeley | Further Information

{"title"=>"Inductive game theory and the dynamics of animal conflict", "type"=>"journal", "authors"=>[{"first_name"=>"Simon", "last_name"=>"DeDeo", "scopus_author_id"=>"6603225099"}, {"first_name"=>"David C.", "last_name"=>"Krakauer", "scopus_author_id"=>"7007174357"}, {"first_name"=>"Jessica C.", "last_name"=>"Flack", "scopus_author_id"=>"8624477500"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "arxiv"=>"1006.5469", "scopus"=>"2-s2.0-77955482930", "sgr"=>"77955482930", "pui"=>"359337504", "isbn"=>"1553-734X", "pmid"=>"20485557", "doi"=>"10.1371/journal.pcbi.1000782"}, "id"=>"972b1435-f13a-3fa7-bae3-7601054eccfc", "abstract"=>"Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management.", "link"=>"http://www.mendeley.com/research/inductive-game-theory-dynamics-animal-conflict", "reader_count"=>128, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>10, "Student > Doctoral Student"=>10, "Researcher"=>35, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>10, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>8}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>10, "Student > Doctoral Student"=>10, "Researcher"=>35, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>5, "Student > Bachelor"=>10, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>42, "Arts and Humanities"=>2, "Business, Management and Accounting"=>5, "Computer Science"=>11, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>4, "Engineering"=>3, "Environmental Science"=>6, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>1, "Materials Science"=>1, "Mathematics"=>7, "Medicine and Dentistry"=>3, "Neuroscience"=>2, "Physics and Astronomy"=>10, "Psychology"=>8, "Social Sciences"=>14, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>14}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>10}, "Psychology"=>{"Psychology"=>8}, "Mathematics"=>{"Mathematics"=>7}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>6}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>3}, "Neuroscience"=>{"Neuroscience"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>42}, "Computer Science"=>{"Computer Science"=>11}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>5}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"United States"=>11, "Japan"=>1, "Portugal"=>1, "Spain"=>1, "India"=>1, "New Zealand"=>1, "Canada"=>1, "Pakistan"=>1, "Belgium"=>1, "Luxembourg"=>1, "Italy"=>1, "Nigeria"=>1, "France"=>1, "Germany"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/849310"], "description"=>"<p>All strategies live in the space of 1-step Markov transition functions. Starting with the simplest model class , we can add individuals to either the first or second fight, systematically building up strategies of increasing complexity based on cognitive, coordination, and computational requirements.</p>", "links"=>[], "tags"=>["classification"], "article_id"=>519778, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lattice_classification_of_strategy_space_/519778", "title"=>"Lattice classification of strategy space.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 02:42:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/849637"], "description"=>"<p>Shown are box plots for the mean frequency of contact aggression received per individual for conflicts of a given size. Conflict sizes were binned so that each category contained an approximately equivalent number of events and to reflect natural categories (<i>e.g.</i> pairs and triplets). The heavy black horizontal line in each plot shows the median “mean value”. The bottom and top of the box give the 25th and 75th percentiles, respectively. The vertical dashed lines show 1.5 times the interquartile range (roughly two standard deviations). The points are outliers, defined as 1.5 times the interquartile range above the third quartile. Note that redirection, by definition, is not possible in conflicts smaller than triplets. Adjacent pairs of fight sizes were compared using the Wilcoxon signed ranks test to determine whether the probability of aggression received increases with fight size. The stars indicate the level of significance for differences between adjacent fight sizes.</p>", "links"=>[], "tags"=>["fights"], "article_id"=>520103, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Large_fights_cost_more_8211_increased_contact_aggression_/520103", "title"=>"Large fights cost more – increased contact aggression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 00:01:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/849161"], "description"=>"<p>Begins at 12:00 hours, and ends just after 20:00 hours. Plotted on the y-axis as “Total Fight Size” is the number of conflict participants per conflict, regardless of whether the participant was an aggressor, recipient, or intervener. The graph gives a sense of the distribution of conflict sizes, and conflict lengths, and the distribution of intervening peaceful periods. Hatched bars indicate periods without data collection.</p>", "links"=>[], "tags"=>["time-series"], "article_id"=>519625, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conflict_event_time_series_data_from_one_observation_period_/519625", "title"=>"Conflict event time-series data from one observation period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 02:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/849710"], "description"=>"<p>For the incoming pair , two outgoing names (here, and ) are chosen. The values of the two associated , and , are swapped. New names are chosen and the process repeated until all the s associated with the incoming pair have been reassigned. This then is done for all possible incoming pairs, and the resultant set used to generate conflict cascades.</p>", "links"=>[], "tags"=>["schematic", "triadic"], "article_id"=>520172, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g007", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_schematic_illustration_of_one_of_the_Triadic_tests_the_Outgoing_Shuffle_/520172", "title"=>"A schematic illustration of one of the Triadic tests – the <i>Outgoing Shuffle</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 00:02:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/849784"], "description"=>"<p>68% confidence are shown. In blue is the base model. In orange, a simulation based on strategies that have been shuffled relative to the base model (<i>Total Shuffle</i>.) In green is a simulation based on strategies where only the incoming pairs have been shuffled relative to the base model (<i>Incoming Shuffle</i>.) In red are the data. Both variants of the base, reliant on triadic decision-making, lie nearer to the data than those strategies of <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000782#pcbi-1000782-g004\" target=\"_blank\">Fig. 4</a>, but still neither are a better fit to the data.</p>", "links"=>[], "tags"=>["Computational biology", "Evolutionary biology", "evolutionary biology/animal behavior"], "article_id"=>520242, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g008", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_sensitivity_of_the_Long_Fraction_to_AND_model_variants_/520242", "title"=>"The sensitivity of the Long Fraction to AND model variants.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 00:04:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/849871"], "description"=>"<p>Overall as a function of power score, showing how the highest and lowest-power groups are fit least well by the + AND strategy assumptions. The 48 individuals are here grouped into units of eight by similarity in power score.</p>", "links"=>[], "tags"=>["triadic"], "article_id"=>520338, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g009", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Going_beyond_triadic_discrimination_/520338", "title"=>"Going beyond triadic discrimination.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 00:05:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/849238"], "description"=>"<p> (as defined in Eq. 1) positive is denoted as a solid line, and negative as a dashed line; arrows denote the forward direction of time. Edges with above 6% (at 95% confidence) are shown; note that detections of different edges are not independent. Node color indicates frequency, with blue meaning rare in fights, and red, frequent.</p>", "links"=>[], "tags"=>["strongest", "correlations", "shown", "directed", "edges"], "article_id"=>519699, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_network_of_the_strongest_correlations_detected_in_the_data_set_shown_as_directed_edges_between_individuals_/519699", "title"=>"The network of the strongest correlations detected in the data set, shown as directed edges between individuals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 02:41:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/849462"], "description"=>"<p>This plot shows the distribution of fight sizes in the real data (red line) and the simulated distributions under each hypothesis. We plot the “long fraction,” the number of fights of a certain size, divided by the number of fights larger than two participants. In <b>green</b> is shown the 95% confidence contours for + OR; the model is unable to generate conflicts of sizes much larger than three. The stricter variant, + AND, performs even more poorly. In <b>orange</b> is shown + OR. Its distribution has a significant fraction of conflicts larger than eight individuals. In <b>yellow</b> is + AND. Even though this is the “conflict-averse” variant of , it produces many large fights over time such that the distribution is “inverted” and there are more large fights than small fights. with the more “conflict prone” OR combinator produces even larger cascades that grow so quickly good statistics become computationally impossible. Dark <b>blue</b> is the 95% contour and light blue is the 68% contour for the distribution generated by + AND, the only model that can capture important features of the data. This triadic strategy cannot be decomposed into pairwise strategies.</p>", "links"=>[], "tags"=>["triadic", "decision-making", "produces", "turbulent"], "article_id"=>519924, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individuals_play_triadic_not_pairwise_strategies_and_it_is_this_triadic_decision_making_that_produces_turbulent_periods_/519924", "title"=>"Individuals play triadic, not pairwise, strategies, and it is this triadic decision-making that produces turbulent periods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 02:45:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/849968"], "description"=>"<p>In nearly all cases, the model outperforms the various “shuffled” alternatives, indicating that the triadic nature of the strategies is central to conflict dynamics. The effect of coarse graining the strategies is to reduce correlations; as the number of levels increases and thus finer distinctions are made, the effects disappear. The data suggest that (two positive, and two negative, levels) are sufficient to reproduce much of the group structure.</p>", "links"=>[], "tags"=>["coefficients"], "article_id"=>520428, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.t001", "stats"=>{"downloads"=>7, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pearson_correlation_coefficients_for_the_model_variants_/520428", "title"=>"Pearson correlation coefficients for the model variants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-05-13 00:07:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/849556"], "description"=>"<p>Shown are box plots for the mean frequency of redirected aggression received per individual for conflicts of a given size. Conflict sizes were binned so that each category contained an approximately equivalent number of events and to reflect natural categories (<i>e.g.</i> pairs and triplets). The heavy black horizontal line in each plot shows the median “mean value”. The bottom and top of the box give the 25th and 75th percentiles, respectively. The vertical dashed lines show 1.5 times the interquartile range (roughly two standard deviations). The points are outliers, defined as 1.5 times the interquartile range above the third quartile. Note that redirection, by definition, is not possible in conflicts smaller than triplets. Adjacent pairs of fight sizes were compared using the Wilcoxon signed ranks test to determine whether the probability of aggression received increases with fight size. The stars indicate the level of significance for differences between adjacent fight sizes.</p>", "links"=>[], "tags"=>["fights", "redirected"], "article_id"=>520024, "categories"=>["Biological Sciences", "Infectious Diseases", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Large_fights_cost_more_8211_increased_redirected_aggression_/520024", "title"=>"Large fights cost more – increased redirected aggression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-05-13 00:00:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/422556"], "description"=>"<div><p>Conflict destabilizes social interactions and impedes cooperation at multiple scales of biological organization. Of fundamental interest are the causes of turbulent periods of conflict. We analyze conflict dynamics in an monkey society model system. We develop a technique, Inductive Game Theory, to extract directly from time-series data the decision-making strategies used by individuals and groups. This technique uses Monte Carlo simulation to test alternative causal models of conflict dynamics. We find individuals base their decision to fight on memory of social factors, not on short timescale ecological resource competition. Furthermore, the social assessments on which these decisions are based are triadic (self in relation to another pair of individuals), not pairwise. We show that this triadic decision making causes long conflict cascades and that there is a high population cost of the large fights associated with these cascades. These results suggest that individual agency has been over-emphasized in the social evolution of complex aggregates, and that pair-wise formalisms are inadequate. An appreciation of the empirical foundations of the collective dynamics of conflict is a crucial step towards its effective management.</p></div>", "links"=>[], "tags"=>["inductive"], "article_id"=>143453, "categories"=>["Biological Sciences", "Cancer", "Evolutionary Biology"], "users"=>["Simon DeDeo", "David C. Krakauer", "Jessica C. Flack"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000782", "stats"=>{"downloads"=>32, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Inductive_Game_Theory_and_the_Dynamics_of_Animal_Conflict/143453", "title"=>"Inductive Game Theory and the Dynamics of Animal Conflict", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-05-13 00:57:33"}

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